Conventional database systems are capable of storing relatively large quantities of data. Businesses, individuals, and other entities may wish to utilize such data, in order to achieve some desired goal. However, as a practical matter, such entities may be unable or unwilling to deploy computational resources which are sufficient to process the data in a timely, accurate, cost-effective, and/or efficient manner.
For example, businesses may maintain transaction databases which record individual transactions conducted between the business and its various customers. Such a business may wish to analyze its transaction data, e.g., with the intention of increasing future profits. For example, a business may wish to analyze its transaction database for the purpose of recommending particular products/services for sale to individual existing/potential customers. In this way, the business may increase sales by ensuring that potential purchasers are presented with opportunities to purchase products/services that are of particular interest or use to them.
However, as referenced above, it may be difficult or impossible for such businesses to generate such recommendations in a manner which is sufficiently fast, accurate, cost-effective, and/or otherwise efficient. As a result, it may be difficult for such businesses to generate desired recommendations in a sufficiently timely manner. Consequently, such businesses may be limited in their ability to achieve desired levels of profit, and/or desired levels of customer satisfaction.